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Software Engineering fundamentals matter more than ever Comments via rhonabwy.com via Yogthos

What's missing to have reproducible builds on PyPI Comments via snarky.ca via ubernostrum

Aiki: Alpha Milestone 26 Update Comments via decuser.github.io by decuser

What do you think of LazyPromise as a lightweight alternative to Effect? Comments via github.com by ivan7237d

A Programming Paradigm for Spatiotemporal Composability Comments via github.com via mandeep

Zig Day Seattle, WA Comments via zig.day via jaredkrinke

Recap: Software Should Work 2026 Comments via bencornia.com via rmpr

Power Relations Comments via corbinsimpson.com by Corbin

Ironies of Automation (1983) This paper discusses the ways in which automation of industrial processes may expand rather than eliminate problems with the human operator. Some comments will be made on methods of alleviating these problems within the "classic' approach of leaving the operator with responsibility for abnormal conditions, and on the potential for continued use of the human operator for on-line decision-making within human-computer collaboration.Comments via ckrybus.com via milkandcigarettes

Are Latent Reasoning Models Easily Interpretable? Models normally do all their reasoning in a continuous hidden state instead of spitting out readable text which makes them hard to monitor. The authors tested the Coconut and CODI models and it turns out these models barely even use their hidden reasoning steps for logical tasks like PrOntoQA and ProsQA. You can force the models to stop thinking early and they almost always spit out the same response anyway. It turns out that their high performance on logical tasks actually comes from their specific training data rather than the extra thinking during inference.Things get even more interesting when the models actually need those reasoning tokens for math problems. The researchers wanted to know if standard step-by-step math solutions were hidden inside the latent space, and projected the hidden states back into regular vocabulary words to check. And sure enough when the models got the math problem right the researchers found the correct intermediate math steps in their hidden states up to 93% of the time. The finding strongly suggests that the models are basically doing standard math steps in the background.They confirmed the exact math operations taking place by tweaking numbers in the prompt and seeing how the hidden states reacted which allowed decoding a verified reasoning path for a large majority of correct predictions. But they could rarely do this for incorrect predictions proving that models are actually way more interpretable than the AI community assumed. And you can even use that interpretability as a signal to guess if the model is about to give a right or wrong answer.Comments via arxiv.org via Yogthos

Qwen3.8-27B - Release Day Demos Comments via loktar00.github.io via Yogthos

Why I remain a skeptic Comments via blog.jsbarretto.com via jbauer

Flirt: GitHub and Mailing List backends Comments via blog.buenzli.dev by senekor

Everything is about to “go dark” Comments via blog.cryptographyengineering.com via untitaker

How Is Compression Prediction? Comments via lukefleed.xyz by lukefleed

Serokell’s Work on GHC: Dependent Types, Part 5 Comments via serokell.io via ehamberg

The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software (2005) Comments via gotw.ca via pushcx